K-Means Clustering Algorithm Implementation and Visualization in MATLAB

This article provides a comprehensive guide to implementing the k-means clustering algorithm using MATLAB, complete with detailed code annotations and visualization examples. We demonstrate the complete workflow from data processing to result visualization, including key MATLAB functions such as kmeans() for cluster assignment and various plotting functions for result interpretation. The implementation covers essential algorithmic components including centroid initialization methods, distance calculation using Euclidean metrics, and iterative convergence checks. We address common challenges encountered during data processing and present practical solutions, such as handling empty clusters and determining optimal k-values through elbow method analysis. The included visualizations showcase cluster separation quality and centroid movement patterns throughout the optimization process. This resource serves as valuable reference for readers interested in data processing and machine learning, offering both theoretical understanding and practical implementation guidance for applying k-means clustering effectively.